This recipe provides a time-efficient way to determine whether you've saturated your sequencing depth, i.e. how much new information is likely to arrive with your next set of sequencing reads. It does so by using digital normalization to generate a "collector's curve" of information collection.
Uses for this recipe …
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Inspired by Sarah Bisbing's excellent post on her first year as a faculty member, here are the questions I remember asking myself during my first six years:
Year 0: What science do I want to do?
Year 1: What the hell am I doing all day and why am I …
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The below is a recipe for subsetting a high-coverage data set to a given average coverage. This differs from digital normalization because the relative abundances of reads should be maintained -- what changes is the average coverage across all the reads.
Uses for this recipe include subsampling reads from a super-high …
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In recent days, we've gotten several requests, including two or three on the khmer mailing list, for ways to extract shotgun reads based on their coverage with respect to the reference. This is fairly easy if you have an assembled genome, but what if you want to avoid doing an …
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I just finished reading Svante Paabo's autobiography, Neanderthal Man: In Search of Lost Genomes. The book is perfect -- if you're a biologist of any kind, you'll understand most of it without any trouble, and even physicists can probably get a lot out of the story (heh).
The book describes Svante …
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Every month, Bjorn Ostman finds another sucker^W^W^W organizes a Carnival of Evolution blog post, that does a roundup of blogs on evolution from a previous month. This month, I'm hosting it -- it's a bit late, due to some teaching duties, so apologies!
Trigger warning: This blog post …
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This past weekend, I accepted an offer to join UC Davis as an Associate Professor of Genetics in the Department of Population Health and Reproduction, in the School of Veterinary Medicine. The appointment is still pending tenure review, but I expect to join Davis whether or not they give me …
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